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** Article: Gender Differences in Multidimensional Material Deprivation)					**
**			A Comparative Analysis of the Role of Household Composition and Education Level	**
** Journal: Heliyon																			**
** Date:	April 2025		 																**
** Authors:	Hooghe, De Grauwe, & Stiers														**
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*** Open data ***
use "Replication_data.dta"


*** Main analyses ***

***Table 2
*Create estimation sample
regress material_deprivation female age education i.employment_status  children i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.B_COUNTRY 
gen sample1=1 if e(sample)

*Model 1
mixed material_deprivation i.female || B_COUNTRY: if sample1==1
*Model 2
mixed material_deprivation i.female age education GDP_Capita seats_women|| B_COUNTRY: if sample1==1
*Model 3
mixed material_deprivation i.female age education i.employment_status children GDP_Capita seats_women|| B_COUNTRY: if sample1==1
*Mpdel 4
mixed material_deprivation i.female age education i.employment_status children GDP_Capita seats_women i.employment_status_spouse i.chief_earner|| B_COUNTRY: if sample1==1


***Table 3
*interaction gender*age
mixed material_deprivation education i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.female##c.age|| B_COUNTRY: if sample1==1

*interaction gender*educational level
mixed material_deprivation age children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.female##c.education|| B_COUNTRY: if sample1==1

*interaction gender*working status R
mixed material_deprivation age education children i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.female##i.employment_status|| B_COUNTRY: if sample1==1

*interaction gender*working status spouse
mixed material_deprivation age education children i.employment_status i.chief_earner GDP_Capita seats_women i.female##i.employment_status_spouse|| B_COUNTRY: if sample1==1

*interaction gender*chief_earner R or not
mixed material_deprivation age education children i.employment_status i.employment_status_spouse GDP_Capita seats_women i.female##i.chief_earner || B_COUNTRY: if sample1==1

*interaction gender*children
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.female##c.children|| B_COUNTRY: if sample1==1

*interaction gender*GDP_Capita
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner seats_women i.female##c.GDP_Capita|| B_COUNTRY: if sample1==1

*interaction gender*seats_women
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita i.female##c.seats_women || B_COUNTRY: if sample1==1


***Table 4
mixed material_deprivation age education children GDP_Capita seats_women i.female##i.chief_earner i.female##i.employment_status i.female##i.employment_status_spouse || B_COUNTRY:  if sample1==1
//Figure 1
margins female , at(chief_earner=(0 1))
marginsplot, scheme(s2mono) xscale(range(-0.2 1.2)) xlabel(0 "No" 1 "Yes") xtitle("Main wage earner") ytitle("Material deprivation") title("") plot1opts(mcol(green) lcol(green)) ci1opts(lcol(green)) plot2opts(mcol(lavender) lcol(lavender)) ci2opts(lcol(lavender)) plotopts(lwidth(thick)) ciopts(lwidth(thick)) legend(pos(6) r(1))

*** Appendices ***

***Appendix B	//RESULTATEN KOMEN NIET OVEREEN
*Model all countries
mixed material_deprivation i.female age education i.employment_status i.employment_status_spouse i.chief_earner children GDP_Capita seats_women  i.female#i(7).employment_status i.female#i(5).employment_status i.female#chief_earner || B_COUNTRY: if sample1==1

*Model high-income countries
mixed material_deprivation i.female age education i.employment_status i.employment_status_spouse i.chief_earner children seats_women i.female#i(7).employment_status i.female#i(5).employment_status i.female#chief_earner || B_COUNTRY: if sample1==1 & High_income==1

*Model low-income countries
mixed material_deprivation i.female age education i.employment_status i.employment_status_spouse i.chief_earner children  seats_women i.female#i(7).employment_status i.female#i(5).employment_status i.female#chief_earner || B_COUNTRY: if sample1==1 & High_income==0
	

***Appendix C
*Generate estimation sample
regress material_deprivation_alt i.female age education i.employment_status  children i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.B_COUNTRY 
gen sample2=1 if e(sample)

*Table C.1
mixed material_deprivation_alt i.female || B_COUNTRY: if sample2==1
mixed material_deprivation_alt i.female age education GDP_Capita seats_women|| B_COUNTRY: if sample2==1
mixed material_deprivation_alt i.female age education i.employment_status children GDP_Capita seats_women|| B_COUNTRY: if sample2==1
mixed material_deprivation_alt i.female age education i.employment_status children GDP_Capita seats_women i.employment_status_spouse i.chief_earner|| B_COUNTRY: if sample2==1

*Table C.2
mixed material_deprivation_alt education i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.female##c.age|| B_COUNTRY: if sample2==1
mixed material_deprivation_alt age children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.female##c.education|| B_COUNTRY: if sample2==1
mixed material_deprivation_alt age education children i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.female##i.employment_status|| B_COUNTRY: if sample2==1
mixed material_deprivation_alt age education children i.employment_status i.chief_earner GDP_Capita seats_women i.female##i.employment_status_spouse|| B_COUNTRY: if sample2==1
mixed material_deprivation_alt age education children i.employment_status i.employment_status_spouse GDP_Capita seats_women i.female##i.chief_earner || B_COUNTRY: if sample2==1
mixed material_deprivation_alt age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women i.female##c.children|| B_COUNTRY: if sample2==1
mixed material_deprivation_alt age education children i.employment_status i.employment_status_spouse i.chief_earner seats_women i.female##c.GDP_Capita|| B_COUNTRY: if sample2==1
mixed material_deprivation_alt age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita i.female##c.seats_women || B_COUNTRY: if sample2==1

*Table C.3
mixed material_deprivation_alt age education children GDP_Capita seats_women i.female##i.chief_earner i.female##i.employment_status i.female##i.employment_status_spouse || B_COUNTRY:  if sample2==1
est store m1
//Figure C.1
margins female , at(chief_earner=(0 1))
marginsplot, scheme(s2mono) xscale(range(-0.2 1.2)) xlabel(0 "No" 1 "Yes") xtitle("Main wage earner") ytitle("Material deprivation") title("") plot1opts(mcol(green) lcol(green)) ci1opts(lcol(green)) plot2opts(mcol(lavender) lcol(lavender)) ci2opts(lcol(lavender)) plotopts(lwidth(thick)) ciopts(lwidth(thick)) legend(pos(6) r(1))

***Appendix D
*Table D.1
mixed material_deprivation i.female || B_COUNTRY: if sample1==1
mixed material_deprivation i.female age education GDP_Capita seats_women employmentdifference || B_COUNTRY: if sample1==1
mixed material_deprivation i.female age education i.employment_status children GDP_Capita seats_women employmentdifference || B_COUNTRY: if sample1==1
mixed material_deprivation i.female age education i.employment_status children GDP_Capita seats_women employmentdifference i.employment_status_spouse i.chief_earner|| B_COUNTRY: if sample1==1

*Table D.2
mixed material_deprivation education i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women employmentdifference i.female##c.age|| B_COUNTRY: if sample1==1
mixed material_deprivation age children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women employmentdifference  i.female##c.education|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status_spouse i.chief_earner GDP_Capita seats_women employmentdifference i.female##i.employment_status|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.chief_earner GDP_Capita seats_women employmentdifference i.female##i.employment_status_spouse|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse GDP_Capita seats_women employmentdifference i.female##i.chief_earner || B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita employmentdifference seats_women i.female##c.children|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner seats_women employmentdifference i.female##c.GDP_Capita|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita employmentdifference i.female##c.seats_women || B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita employmentdifference i.female##c.employmentdifference || B_COUNTRY: if sample1==1

***Table D.4
mixed material_deprivation age education children GDP_Capita seats_women employmentdifference i.female##i.chief_earner i.female##i.employment_status i.female##i.employment_status_spouse || B_COUNTRY:  if sample1==1
//Figure D.1
margins female , at(chief_earner=(0 1))
marginsplot, scheme(s2mono) xscale(range(-0.2 1.2)) xlabel(0 "No" 1 "Yes") xtitle("Main wage earner") ytitle("Material deprivation") title("") plot1opts(mcol(green) lcol(green)) ci1opts(lcol(green)) plot2opts(mcol(lavender) lcol(lavender)) ci2opts(lcol(lavender))  plotopts(lwidth(thick)) ciopts(lwidth(thick)) legend(pos(6) r(1))


***Appendix E
*Table E.1
mixed material_deprivation i.female || B_COUNTRY: if sample1==1
mixed material_deprivation i.female age education GDP_Capita seats_women gender_equality || B_COUNTRY: if sample1==1
mixed material_deprivation i.female age education i.employment_status children GDP_Capita seats_women fender_equality  || B_COUNTRY: if sample1==1
mixed material_deprivation i.female age education i.employment_status children GDP_Capita seats_women gender_equality  i.employment_status_spouse i.chief_earner|| B_COUNTRY: if sample1==1

*Table E.2
mixed material_deprivation education i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women gender_equality  i.female##c.age|| B_COUNTRY: if sample1==1
mixed material_deprivation age children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita seats_women gender_equality   i.female##c.education|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status_spouse i.chief_earner GDP_Capita seats_women gender_equality  i.female##i.employment_status|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.chief_earner GDP_Capita seats_women gender_equality  i.female##i.employment_status_spouse|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse GDP_Capita seats_women gender_equality  i.female##i.chief_earner || B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita gender_equality  seats_women i.female##c.children|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner seats_women gender_equality  i.female##c.GDP_Capita|| B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita gender_equality  i.female##c.seats_women || B_COUNTRY: if sample1==1
mixed material_deprivation age education children i.employment_status i.employment_status_spouse i.chief_earner GDP_Capita gender_equality  i.female##c.gender_equality  || B_COUNTRY: if sample1==1

*Table E.3
mixed material_deprivation age education children GDP_Capita seats_women gender_equality  i.female##i.chief_earner i.female##i.employment_status i.female##i.employment_status_spouse || B_COUNTRY:  if sample1==1
//Figure E.1
margins female , at(chief_earner=(0 1))
marginsplot, scheme(s2mono) xscale(range(-0.2 1.2)) xlabel(0 "No" 1 "Yes") xtitle("Main wage earner") ytitle("Material deprivation") title("") plot1opts(mcol(green) lcol(green)) ci1opts(lcol(green)) plot2opts(mcol(lavender) lcol(lavender)) ci2opts(lcol(lavender)) plotopts(lwidth(thick)) ciopts(lwidth(thick)) legend(pos(6) r(1))
